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Classification of Serranidae Species Using Color Based Statistical Features

Bilal İşçimen*, Yakup Kutlu, Cemal Turan

DOI: 10.28978/nesciences.292352


Classification, Serranidae family, HSV, color, texture

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  • Alsmadi, M. K., Omar, K. B., Noah, S.A., Almarashdeh, I. (2010). Fish recognition based on robust features extraction from size and shape measurements using neural network. Journal of Computer Science, 6 (10): 1088.
  • Alsmadi, M. K., Omar, K.B., Noah, S.A., Almarashdeh, I. (2010). Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier. Journal of Theoritical and Applied Information Technology.
  • Atasoy, H., Yildirim, E., Kutlu, Y., Tohma, K. (2015). Webcam Based Real-Time Robust Optical Mark Recognition. International Conference on Neural Information Processing. Springer International Publishing, pp. 449-456.
  • Benson, B., Cho, J., Goshorn, D., Kastner, R. (2009). Field programmable gate array (FPGA) based fish detection using Haar classifiers. American Academy of Underwater Sciences.
  • Bothmann, L., Windmann, M., Kauermann, G. (2016). Realtime classification of fish in underwater sonar videos. Journal of the Royal Statistical Society: Series C (Applied Statistics).
  • Cabreira, A.G., Tripode, M., Madirolas, A. (2009). Artificial neural networks for fish-species identification. ICES Journal of Marine Science: Journal du Conseil.
  • Chuang, M. C., Hwang, J. N., Williams, K. (2016). A Feature Learning and Object Recognition Framework for Underwater Fish Images. IEEE Transactions on Image Processing, 25(4): 1862-1872.
  • Chuang, M.C., Hwang, J.N., Kuo, F.F., Shan, M.K., Williams, K. (2014). Recognizing live fish species by hierarchical partial classification based on the exponential benefit. IEEE International Conference on Image Processing (ICIP), pp. 5232-5236.
  • Cunningham, P., Delany, S.J. (2007). k-Nearest neighbour classifiers.Multiple Classifier Systems, 1-17.
  • D’Elia, M., Patti, B., Bonanno, A., Fontana, I., Giacalone, G., Basilone, G., Fernandes, P.G. (2014). Analysis of backscatter properties and application of classification procedures for the identification of small pelagic fish species in the Central Mediterranean. Fisheries Research, 149, 33-42.
  • Daramola, S.A., Omololu, O. (2016). Fish Classification Algorithm using Single Value Decomposition. International Journal of Innovative Research in Science, Engineering and Technology, 5 (2): 1621-1626.
  • Fabic, J. N., Turla, I. E., Capacillo, J.A., David, L.T., Naval, P.C. (2013). Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis. Underwater Technology Symposium (UT), IEEE International, pp. 1-6.
  • Forsyth, D.A. and Ponce, J. (2002). Computer vision: a modern approach, Prentice Hall Professional Technical Reference, ch. 15.
  • Hu, J., Li, D., Duan, Q., Han, Y., Chen, G., Si, X. (2012). Fish species classification by color, texture and multi-class support vector machine using computer vision. Computers and Electronics in Agriculture, 88, 133-140.
  • Huang, P.X., Boom, B.J., Fisher, R.B. (2015). Hierarchical classification with reject option for live fish recognition. Machine Vision and Applications, 26 (1): 89-102.
  • Iscimen, B., Atasoy, H., Kutlu, Y., Yildirim, S., Yildirim, E. (2015). Smart Robot Arm Motion Using Computer Vision. Elektronika ir Elektrotechnika, 21(6): 3-7.
  • Iscimen, B., Kutlu, Y., Reyhaniye, A. N., Turan, C. (2014). Image analysis methods on fish recognition. 22nd Signal Processing and Communications Applications Conference (SIU), pp. 1411-1414.
  • Iscimen, B., Kutlu, Y., Uyan, A., Turan, C. (2015). Classification of fish species with two dorsal fins using centroid-contour distance. 23nd Signal Processing and Communications Applications Conference (SIU), pp. 1981-1984.
  • Manzoor, S., Islam, R. U., Khalid, A., Samad, A., Iqbal, J. (2014). An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation. Robotics and Computer-Integrated Manufacturing, 30 (3): 351-362.
  • Mizuno, K., Liu, X., Asada, A., Ashizawa, J., Fujimoto, Y., Shimada, T. (2015). Application of a high-resolution acoustic video camera to fish classification: An experimental study. Underwater Technology (UT), IEEE, pp. 1-4.
  • Ogunlana, S.O., Olabode, O., Oluwadare, S.A.A., Iwasokun, G.B. (2015). Fish Classification Using Support Vector Machine. African Journal of Computing & ICT, 8(2): 75-82.
  • Shafait, F., Mian, A., Shortis, M., Ghanem, B., Culverhouse, P. F., Edgington, D., Cline, D., Ravanbakhsh, M., Seager, J., Harvey, E. S. (2016). Fish identification from videos captured in uncontrolled underwater environments. ICES Journal of Marine Science: Journal du Conseil, 73 (10): 2737-2746.
  • Turan, C., Erguden, D., Gürlek, M., Yaglioglu, D., Keskin, Ç. (2007). Atlas and systematics of marine Bony fishes of Turkey. Nobel, Adana, Turkey.